Document last updated: 2025-04-15
We extracted and sequenced DNA from a total of 245 samples, comprised of 5 random stool samples from before the experimental diets began (referred to as Day 0 or Week 0), while all mice were fed the their standard “Control-diet”, and then weekly collections of 5 random stool samples per cohort over the next 12 weeks (5 replicates x 4 cohorts x 12 weeks). DNA extractions were performed using []. Taxonomic profiling was performed by sequencing bacterial 16S rRNA genes. The V3-V4 region of bacterial (and archaeal) 16S rRNA genes was amplified using primers 515f-R806 (Bates et al., 2010). PCR amplifications were performed using previously described methods (Mueller et al., 2016). In the first PCR, sample barcoding was performed with forward and reverse primers each containing a 6-bp barcode; 22 cycles with an annealing temperature of 60oC were performed. The second PCR added Illumina adaptors over 10 cycles with an annealing temperature of 65°C. Amplicon clean-up was performed with a 0.9 ratio of AMPure XP beads (Beckman Coulter, Indianapolis IN), following manufacturer’s instructions and final elutions were performed with 30µl Elution Buffer. Following clean-up, samples were quantified with an Invitrogen Quant-iTTM ds DNA Assay Kit on a BioTek Synergy HI Hybrid Reader and pooled at a concentration of 10 ng per sample. A final clean-up step was performed on pooled samples using a 0.9 ratio of AMPure XP beads. Samples were sequenced on an Illumina MiSeq platform with PE250 chemistry at Los Alamos National Laboratory. Unprocessed sequences are available through NCBI’s Sequence Read Archive ().
Bacterial sequences were processed using Usearch11 (Edgar, 2010). Samples were demultiplexed, paired ends merged, quality filtered and globally trimmed using a fastq_maxee threshold of 1.0 (Edgar and Flyvbjerg, 2015), dereplicated, and singletons were removed. Chimeras were removed and 97% OTU clustering was performed independently for the two datasets with the -cluster_otus command using the UPARSE-OTU algorithm (Edgar, 2013). Previous analyses have shown congruent ecological patterns with use of OTUs versus exact sequence variants (ESVs) for delineating microbial taxa (92). OTU tables were created using the -otutab command. Bacterial OTUs were classified using the Ribosomal Database Project (RDP) classifier v.19 (Wang et al., 2007). Next-generation sequencing of 16S rRNA genes resulted in 5,043,233 reads (average of 20,585 ± 3,467 (SD) reads per sample, n = 245 samples). These reads yielded 1,090 OTUs. Domain-level analyses revealed that 99.99% of reads were classified as “Bacteria”, 0.003% as “Eukaryota”, and 0.01% were unclassified at the domain level. The dataset was then filtered to exclude all domains except Bacteria, all reads assigned at >= 80% confidence at the phylum-level (n = 12,991), reads assigned to the class Chloroplast (n = 122), and the remaining singleton reads (n = 32). We rarefied via subsampling without replacement to 13,006 sequences per sample to account for uneven sequencing depth and from that, 624 bacterial OTUs (97% sequence similarity) were identified from 243 samples.
Phylum/Class/Order/Family/Genus-level bar plots of the community profiles by week along the 12 week longitudinal can be found in SI Figures XYZ (or here: Taxonomy.
Microbial community analyses were conducted primarily in the vegan (Oksanen et al., 2018) and phyloseq (McMurdie and Holmes, 2013) packages in the R programming environment unless otherwise noted. Patterns in microbial community composition were visualized using non-metric multidimensional scaling (NMDS) using Bray-Curtis (abundance-weighted) and Jaccard (binary presence/absence) distance metrics.
We investigated the degree to which differences in microbial community profiles were explained by experimental factors ….
We also examined differences across initial …
To quantify inter-individual variability prior to dietary intervention, we analyzed five stool samples collected at baseline (Week-0), before diet assignment. Alpha diversity was measured using both observed OTU richness and Shannon entropy. Taxonomic profiles were summarized at the family level using relative abundance from a transformed OTU table. Pairwise Bray–Curtis dissimilarities were computed to assess baseline community divergence. Additionally, we calculated distances to the group centroid using PERMDISP to quantify β-dispersion. These metrics were used to establish the null range of baseline variation, providing an ecological benchmark against which subsequent compositional shifts were interpreted.
Using the R package betapart (v1.6), we computed the 3 abundance-based multiple-site dissimilarities (balanced variation fraction,abundance-gradient fraction, and overall dissimilarity) using the Bray-Curtis family of dissimilarity indices, in addition to the corresponding presence/absence-based multiple-site dissimilarities accounting for the spatial turnover and the nestedness components of beta diversity, and the sum of both values using beta.multi(index.family = sorensen”). To compute abundance-based beta diversity, we used unrarefied OTU tables to retain quantitative abundance information. Rarefaction can eliminate natural abundance gradients, resulting in inflated balanced dissimilarity and zero-valued gradient components in Bray-Curtis partitioning (Baselga 2017). We therefore retained untransformed counts for βBRAY partitioning, and only applied rarefied tables for presence/absence-based analyses (e.g., βSOR).
The statistical significance of these explanatory experimental factors was assessed using adonis2(), a function based on permANOVA, within the vegan R-package (McArdle and Anderson, 2001). Adonis is a permutational (n = 999) multivariate analysis of variance test that partitions our Bray-Curtis distance matrices among sources of variation (Anderson, 2001).
The distinctness of the … communities was assessed using the Random Forests classification algorithm (Breiman, 2001), using 1000 trees. As implemented in the R package ‘randomForest’, the algorithm constructs each tree using a different bootstrap sample from the original data (approximately 1/3 of the cases are left out of the bootstrap sample and not used in the construction of the kth tree), thus providing an unbiased estimate of the test set error without the need for separate cross-validation test.
Nonalcoholic fatty liver disease (NAFLD) is a rapidly growing global health concern and the most common cause of chronic liver disease worldwide. Its prevalence has risen in parallel with obesity, type 2 diabetes, and metabolic syndrome, and it now affects nearly one in three adults globally. NAFLD encompasses a spectrum of liver pathology, ranging from simple steatosis to nonalcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma. Despite extensive efforts, there are currently no approved pharmacologic therapies for NAFLD, and clinical management remains limited to diet and lifestyle modification.
Over the past decade, the gut microbiota has emerged as a critical regulator of host metabolism and a central player in NAFLD pathogenesis. The gut-liver axis—a bidirectional communication pathway linking the intestinal microbiota and the liver via the portal circulation—enables microbial metabolites, antigens, and endotoxins to directly influence hepatic physiology and immune responses (Li et al., 2017). Perturbations in this axis, particularly those arising from gut microbial dysbiosis and impaired intestinal barrier function, are increasingly recognized as upstream drivers of liver disease.
A growing body of evidence implicates increased intestinal permeability (IP) and the translocation of microbial products, especially lipopolysaccharide (LPS) from Gram-negative bacteria, as key mediators of HFD-induced liver pathology (Ciaula et al., 2020). Elevated LPS levels activate Toll-like receptor 4 (TLR4) on hepatic immune and parenchymal cells, triggering MyD88-dependent inflammatory signaling cascades that promote the production of pro-inflammatory cytokines such as TNF-α and IL-6, thereby exacerbating hepatic steatosis and injury. Importantly, these events are initiated at the level of the intestinal epithelial tight junction (TJ) barrier, whose disruption facilitates microbial translocation and systemic endotoxemia.
Although the association between NAFLD and increased intestinal permeability is well documented, a critical gap in mechanistic understanding remains. Specifically, it is unclear whether a defective intestinal TJ barrier is simply a consequence of liver inflammation or a primary pathogenic factor in NAFLD development. Furthermore, the molecular mechanisms by which HFD induces barrier dysfunction—and whether this process can be therapeutically targeted—remain incompletely defined.
To address these fundamental questions, we investigate the hypothesis that HFD-induced increase in intestinal permeability is a sine qua non pathogenic driver of NAFLD, and that targeted modulation of the intestinal TJ barrier can prevent or reverse liver disease. Our preliminary studies support this model by demonstrating that: (1) increased IP is required for both hepatic steatosis and inflammation, (2) HFD consumption leads to a rapid rise in gut-derived LPS, (3) the LPS-TLR4 signaling axis is essential for both IP and NAFLD development, and (4) HFD upregulates myosin light chain kinase (MLCK), a key effector of TJ regulation, which mediates barrier dysfunction and liver injury.
Strikingly, we also observed that molecular targeting of the intestinal TJ barrier via the probiotic Lactobacillus acidophilus LA1 suppressed MLCK activation, preserved TJ integrity, and prevented disease progression. These findings suggest that probiotic-driven modulation of host-microbiota interactions at the epithelial interface offers a novel therapeutic avenue for the treatment of NAFLD.
To test these mechanistic hypotheses, we employed a controlled experimental design comprising four mouse cohorts: (1) control-diet-fed mice, (2) wild-type mice fed a high-fat diet (HFD), (3) HFD-fed wild-type mice supplemented with L. acidophilus LA1 (HFD-LA1), and (4) HFD-fed Villin-Cre TLR4/MyD88-deficient mice with impaired epithelial innate immune signaling. This approach allows us to dissect the individual and combined contributions of diet, microbiota, and host immune sensing to the breakdown of intestinal barrier function and the development of NAFLD.
By integrating microbiome analysis with host immune and epithelial phenotyping, our study aims to clarify the causal role of the intestinal barrier in HFD-induced NAFLD and to explore whether therapeutic strengthening of this barrier represents a viable strategy to halt or reverse disease progression.
We first used a community ecology framework to assess … Our [analytical aims] were 1) characterize the baseline community variation … 2) assess temporal trajecories within and among diet cohorts 3) build off the previous to narrow in on key HFD-driven, temporally-relevant, perburations in community structure. By partitioning community dissimilarity [… explain betapart rationale] … Following Mori, Isbell, and Seidl (2018), we treat β-diversity not just as a pattern, but as a mechanistic lens to infer how microbial community assembly processes shape functional outcomes under …diets… over time.”
Sections goals:
the baseline compositional heterogeneity (or lack thereof) among samples that began with identical conditions.
This is important because: - It defines how much divergence we expect by chance or stochasticity - allows us to say later, “This diet/timepoint exceeded initial inter-individual variability” - It gives us a framework to talk about assembly (even at T0)
Add stability of control diet over time
consider all control-diet OTUs
Comparing to full dataset/across all weeks
The initial/starting communities (n = 5) contained over half of all of the OTUs detected across the whole dataset (364 of the total 624 OTUs, or 58.3%). Each of the 5 random Week-0 stool samples were comprised of 301 to 311 OTUs.
Families Turicibacteraceae and Rikenellaceae and genera Alistipes, Duncaniella (G-), Limosilactobacillus, and Turicibacter were top 10 for Week-0, but not whole dataset. Conversely, Bacteroidaceae and Bifidobacteriaceae were not in the top 10 families for Week-0, nor were Bifidobacterium, Faecalibaculumm, uncl_Erysipelotrichaceae, or uncl_Oscillospiraceae in the top 10 genera for Week-0.
These communities had an overall abundance-based multiple-site dissimilarity of 0.175 (betapart::beta.multi.abund()) and a presence/absence-based total multiple-site dissimilarity of 0.204 (betapart::beta.multi()). The turnover or species replacement component, measured as the Simpson dissimilarity, represented 93.99% of the total presence/absence-based dissimilarity. Pairwise comparisons of Week-0 communities showed that a minimum 23 of and a maximum of 30 OTUs were not shared between pairs of these initial samples.
The low dissimilarity values of Week-0 samples (abundance-based multiple-site dissimilarity of 0.175 (betapart::beta.multi.abund()) and a presence/absence-based total multiple-site dissimilarity of 0.204) indicates that the baseline communities were relatively homogeneous. Closer examination of these differences revealed that the vast majority, 93.99%, of dissimilarity was attributable to turnover, suggesting that even at baseline, species replacement — not simple richness differences — was the primary mechanism differentiating these microbiomes. This level of heterogeneity at T0 provides a critical context: downstream changes due to diet must exceed this baseline variability to be considered biologically meaningful. This Week-0 landscape acts as a null model against which future community divergence (due to diet and time) can be compared, and should be factored into interpretation of assembly trajectories.
Goals here:
1. Show [macro-level] microbial community structure shifts under HFD.
2. Highlight how dominant vs. low-abundance taxa change across groups (e.g., HFD vs. control).
3. Establish whether certain taxonomic tiers (e.g., >10%) become more/less dominant.
4. Lay groundwork for linking those shifts to intestinal barrier defects or downstream disease.
Figure 3.1: Figure X. Community structure and compositional variability among Week-0 microbiotas. (A) Family-level taxonomic profiles are relatively consistent across five pre-intervention stool samples, with communities dominated by Lactobacillaceae, Muribaculaceae, unclassified Bacteroidales, Lachnospiraceae (all families with a >10% mean relative abundance).(B - old) Observed richness ranged from 301 to 311 OTUs (mean = 304.2), and Shannon entropy ranged from 4.31 to 4.39 (mean = 4.35), indicating modest baseline heterogeneity. (C) Pairwise Bray–Curtis dissimilarities ranged from 0.06 to 0.08 (mean = 0.07), defining the magnitude of inter-individual variation at baseline. (D) Distance to group centroid (mean = 0.044) quantifies beta dispersion under shared, pre-intervention conditions. These data define a reference distribution of compositional variability (/baseline variablility) that contextualizes subsequent changes under dietary exposure.
Sections goals:
plots are in SI… Temporal beta diversity trends within cohorts
Add/switch to shared OTUs among baseline/control by week
(still need to clean up legends, spacing, etc)
To evaluate the longitudinal impact of chronic high-fat diet (HFD) exposure and probiotic intervention on gut microbial community structure, we performed non-metric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarities of fecal microbial profiles collected over a 12-week time course (Figure NMDS). The ordination yielded a low stress value (Stress = 0.042), indicating a high-fidelity two-dimensional representation of underlying community distances with excellent preservation of rank-order dissimilarities and high interpretability. NMDS revealed distinct temporal and treatment-specific trajectories in microbial community composition, reflecting both ecological divergence and the influence of diet and host factors on microbial assembly.
In control-diet mice, microbial communities remained tightly clustered throughout the 12-week period, indicating high temporal stability and resistance to compositional drift under baseline dietary conditions. In contrast, wild-type mice fed a chronic HFD exhibited a pronounced, directional trajectory across NMDS space, with progressive separation from week 0 to week 12. This shift denotes a sustained restructuring of the gut microbiome consistent with microbial dysbiosis, likely reflecting the expansion of pathobionts and loss of commensal taxa known to accompany metabolic disease progression.
redundant: Microbiota from control-diet mice remained tightly clustered throughout the time course, with minimal drift across weeks. This tight grouping indicates a high degree of temporal stability and ecological resilience under non-perturbative dietary conditions, suggesting that the gut microbial community remains in a homeostatic state in the absence of dietary or host stressors. - Tight clustering over time. - Minimal drift across weeks. - Stable community structure → consistent microbiota under non-perturbing diet.
In contrast, wild-type mice fed a high-fat diet (HFD) exhibited a striking, directional shift in NMDS space, progressing from week 0 (yellow) to week 12 (dark purple) along both NMDS1 and NMDS2 axes. This progressive divergence suggests a sustained and coordinated community restructuring, consistent with HFD-induced microbial dysbiosis. The trajectory implies gradual depletion of protective taxa (e.g., Firmicutes such as Lachnospiraceae, Bacteroidetes) and enrichment of inflammation-associated microbes (e.g., Proteobacteria), paralleling the development of intestinal permeability and NAFLD phenotypes observed in this group.
- Strong, directional shift along both NMDS1 and NMDS2 axes from week 0 → week 12.
- Suggests progressive microbial dysbiosis driven by chronic HFD.
- This directional community remodeling tracks with disease progression — i.e., NAFLD phenotype and leaky gut.
Mice receiving HFD supplemented with Lactobacillus acidophilus (HFD-LA1) also exhibited early shifts away from baseline community structure; however, this trajectory plateaued mid-way through the experiment and culminated in a stably clustered endpoint distinct from both control and wild-type HFD groups. Notably, the magnitude of divergence from baseline was reduced relative to the wild-type HFD group, indicating that LA1 intervention limited the extent of community remodeling. The tight clustering of later timepoints suggests ecological stabilization, consistent with resistance to full-blown dysbiosis. These findings align with prior observations that LA1 preserves intestinal barrier function and prevents hepatic steatosis, suggesting that LA1 may promote microbiota resilience and functional homeostasis, potentially through enrichment of TJ-supporting or anti-inflammatory taxa. - Early shift away from week 0 community, but then stabilizes into a distinct cluster by mid-late timepoints. - Reduced trajectory compared to wild-type HFD — less total shift in community space. - Suggests LA1 buffers against full-blown HFD-induced dysbiosis. - Implies ecological stabilization and resilience — perhaps maintenance of barrier-supporting taxa (e.g., Akkermansia, Lactobacilli). - Supports idea that LA1 preserves gut homeostasis via TJ integrity + microbiome modulation.
Interestingly, HFD-fed Villin-Cre mice lacking epithelial TLR4/MyD88 signaling (Villin-Cre-HFD) followed a trajectory distinct from both wild-type HFD and HFD-LA1 groups. While these mice also exhibited temporal drift, they progressed to a unique region of NMDS space, clearly separated from all other treatment arms. This distinct clustering pattern supports the conclusion that epithelial TLR4/MyD88 signaling is required for canonical HFD-induced microbial remodeling. In its absence, the gut microbiota responds to dietary fat exposure along an alternative trajectory, resulting in a non-pathogenic microbial configuration that does not support the development of increased intestinal permeability or NAFLD. These results further suggest that host innate immune pathways shape the ecological response of the microbiome to environmental perturbation, and their disruption alters the gut’s microbiota-disease axis. - Also shows a trajectory over time, but it’s clearly distinct from WT-HFD and from LA1-HFD. - Ends in a different region of NMDS space, forming a unique stable endpoint. - Likely reflects how immune signaling shapes host-microbiome cross-talk — TLR signaling is critical for microbial sensing. - The distinct endpoint suggests that even under HFD, microbiota does not follow the canonical path of dysbiosis. - Supports hypothesis that TLR4/MyD88 signaling is required for HFD-induced dysbiotic shift.
Together, these findings demonstrate that chronic HFD induces a reproducible and progressive shift in gut microbial community structure that is dependent on host innate immune signaling and is mitigated by probiotic intervention. Chronic HFD induces a consistent, directional community shift associated with intestinal barrier dysfunction and hepatic inflammation. In contrast, both probiotic intervention and genetic disruption of TLR4/MyD88 signaling redirect microbial succession, yielding distinct and functionally protective community endpoints. These data reinforce the idea that gut microbial ecology is a central node in the pathogenesis of HFD-induced NAFLD and a viable target for therapeutic modulation. The observed patterns provide strong support for a model in which gut microbial ecology is both a driver and a biomarker of intestinal permeability and downstream NAFLD pathogenesis.
Significance:
- HFD fundamentally reshapes the gut microbial community over time, consistent with induction of NAFLD.
- Probiotic LA1 intervention limits this remodeling, stabilizing the microbiome — ecologically and functionally consistent with a protective role.
- Host immune signaling (TLR4/MyD88) is essential for driving or permitting the dysbiotic transition — suggesting a host-microbe co-pathogenic axis.
- NMDS space itself reflects functional microbial ecology:
- WT-HFD ends in “inflammatory” space.
- LA1-HFD stabilizes in a “resilient” space.
- Villin-Cre ends in “non-pathogenic alternative” space.
Prevalence refers to the proportion of HFD samples in which each OTU was detected (>0 abundance).
HFD-Enriched OTUs Across 12 Weeks
To identify bacteria that were enriched in HFD-fed mice relative to baseline, we compared the fecal microbiome of WT HFD-fed mice at each timepoint (Weeks 1–12) to Week-0 samples using two parallel criteria: (1) Wilcoxon tests between groups (FDR-adjusted p < 0.05) and (2) an emergent OTU filter, capturing taxa absent in all control samples but present at ≥25% prevalence and ≥1% mean abundance in HFD mice.
A total of 51 OTUs were identified as significantly enriched in at least one week of HFD exposure. OTUs were further grouped by persistence, defined as the number of weeks they were detected as enriched out of 12 total timepoints. This resulted in:
Notably, highly persistent OTUs accounted for the majority of enriched signals and showed a broader range of abundance and enrichment magnitude over time.
Most highly persistent OTUs (enriched 11-12 weeks):
OTU35, OTU41, OTU90 were persistent 12/12 weeks. OTU28, OTU30, OTU3979, OTU4852, OTU77 were persistent 11/12 weeks.
- OTU28: Bacillota; Clostridia; Eubacteriales; Lachnospiraceae; Dorea; uncl_Dorea - HFD 2.8% vs Control 0.1%
- OTU30: Bacillota; Clostridia; Eubacteriales; Lachnospiraceae; uncl_Lachnospiraceae; uncl_Lachnospiraceae - HFD 1.1% vs Control 0%
- OTU35: Bacillota; Clostridia; Eubacteriales; Lachnospiraceae; uncl_Lachnospiraceae; uncl_Lachnospiraceae - HFD 5% vs Control 0%
- OTU3979: Bacillota; Clostridia; Eubacteriales; Lachnospiraceae; uncl_Lachnospiraceae; uncl_Lachnospiraceae - HFD 1.2% vs Control 0%
- OTU41: Pseudomonadota; Deltaproteobacteria; Desulfovibrionales; Desulfovibrionaceae; uncl_Desulfovibrionaceae; uncl_Desulfovibrionaceae - HFD 1% vs Control 0%
- OTU4852: Bacillota; Erysipelotrichia; Erysipelotrichales; Erysipelotrichaceae; Faecalibaculum; rodentium - HFD 3.3% vs Control 0%
- OTU77: Actinomycetota; Coriobacteriia; Eggerthellales; Eggerthellaceae; Adlercreutzia; caecimuris - HFD 1.4% vs Control 0.4%
- OTU90: Bacteroidota; Bacteroidia; Bacteroidales; uncl_Bacteroidales; uncl_Bacteroidales; uncl_Bacteroidales - HFD 1.6% vs Control 0%
OTUs ranged in mean relative abundance from 1% (minimum threshold for inclusion) to 15.1% in HFD mice, with the most persistent OTUs consistently exceeding 5–10% in abundance. Among the highly persistent group, the most frequently enriched OTUs included:
unclass_Prevotellaceae–OTU458 (mean HFD abundance 2.0%),
unclass_Lachnospiraceae–OTU15 (1.5%),
Blautia–OTU220 (4.3%),
Ligilactobacillus–OTU244 (1.1%), and
unclass_Lactobacillales–OTU70 (1.5%)
These OTUs were enriched across 5 or more weeks, with several showing early detection (Week 1–2) and sustained elevation through Week 12.
Temporal patterns of enrichment varied:
Color mapping of log2 fold change (log2FC) revealed substantial inter-OTU variability in enrichment magnitude, ranging from log2FC ~1 (2-fold change) to values exceeding log2FC = 20. Emerging OTUs — shown as triangles — exhibited especially high fold changes, reflecting their complete absence from baseline controls.
Overall, HFD feeding led to reproducible enrichment of a core set of moderately to highly abundant OTUs, many of which were sustained over multiple timepoints. These results reflect both acute microbiome remodeling in the early phases of HFD exposure and a stabilization of distinct HFD-associated taxa with longer-term feeding.
The consistent detection of certain taxa over time and their high relative abundance suggest that these organisms may play a role in mediating or maintaining the gut environment under chronic dietary fat stress.
Major indicators of disease state and the relevant time periods
Quantitative indicators of steatosis/ steatohepatitis/ fibrosis/ cirrhosis |
Disease (Proxy) Measurements | Control-diet | HFD | HFD-LA | Villin-Cre-HFD |
|---|---|---|---|---|---|
| Overview/Notes | Generally, increased IP by week 4 and disease state by week 6 | No leaky gut phenotype | ? | ||
| Indicator of barrier dysfunction | Serum LPS (in healthy mice: 50-100 pg/mL) - Large molecule, needs defective barrier to cross into bloodstream (via portal vein -> then liver disease) | Based on linear standard curve - Big jumps in weeks 3 & 4, then decreases by still high | |||
| Luminal LPS? | |||||
| Dextran 4kd Flux | Starts to increase vs control in week 3 | ||||
| Dextran 10kd Flux | only week 12 | ||||
| Defective liver function | serum ALT | ||||
| ? | Percentage of weight gain throughout trial |
Alterations in intestinal permeability and related markers
Expression and activity of MLCK
Activation of inflammatory pathways:
Temporal β: - How stable or variable microbial community composition is within each diet each week - Whether dissimilarity is driven more by abundance rearrangement (balanced) or net change/loss (gradient)
Decomposing Bray and Sørensen allows for: - Distinguishing between abundance-driven vs. taxon-presence-driven patterns - Highlighting when community reorganization occurs without richness loss (low nestedness, high turnover) - Detecting when changes are functionally inert (e.g., nestedness without turnover)
Caution: don’t over-interpret Bray…
| Aspect | Bray.Curtis.Plot | Sørensen.Plot |
|---|---|---|
| Dissimilarity Type | Abundance-weighted | Presence/absence-based |
| Component Mechanisms | Balanced reallocation vs. abundance gradients | Species turnover vs. nested loss |
| Ecological Implication | Shifts in abundance hierarchy or biomass | Changes in species identity (turnover or loss) |
Figure 4.1: Figure X. Timepoint-resolved profiles of within-cohort dissimilarity, decomposed by abundance-weighted (Bray–Curtis) and presence/absence-based (Sørensen) β-dissimilarity metrics and their components, with loess fit (solid lines) and weekly means (dotted lines) shown. Data were bootstrapped (n = 1,000), so points are aggregated estimates per week per cohort Each panel shows dissimilarity values derived from bootstrapped 4-sample subsets within each diet cohort at each timepoint (week-0 having only control-diet stool samples). Solid lines indicate loess-smoothed trends across weeks, dotted lines represent the weekly cohort means. All values reflect within-cohort dissimilarity among mice receiving the same diet at the same timepoint. (A) Total abundance-weighted (Bray–Curtis) dissimilarity: Overall community dissimilarity remained relatively stable within most cohorts, with mild increases in some high-fat diet groups over time. The mean abundanced-weighted dissimilarity among all cohorts within the study period was 0.22, with values ranging from 0.09 to 0.53. (B) Balanced variation component: Balanced variation accounted for most within-cohort dissimilarity within each weekly sampling. The majority of Bray-Curtis dissimilarity was attributable to changes in the relative abundance of shared taxa (mean = 0.22), indicating reshuffling within a shared taxonomic framework. (C) Abundance gradient component: Gradient dissimilarity was generally low throughout (max = 0.41), consistent with minimal/limited directional shifts in taxon dominance or loss. (D) Total Sørensen dissimilarity (βSOR): Presence/absence-based dissimilarity showed similar cohort-level trends to abundance-based metrics, capturing taxonomic divergence not driven by abundance. (E) Species turnover (βSIM): The majority of Sørensen dissimilarity (mean of 0.36, or 85.7%) was attributable to species turnover, reflecting taxonomic replacement within timepoints within each cohort. (F) Nestedness-resultant dissimilarity (βSNE): This component contributed minimally (mean = 0.068), indicating that richness loss or gain without taxon replacement was rare. Total dissimilarity remained relatively stable across time within most cohorts. Balanced variation accounted for the majority of observed dissimilarity, consistent with changes in relative abundance of shared taxa rather than directional gain or loss. Abundance gradient dissimilarity was consistently low, with occasional spikes at specific timepoints (notably at Week 5 and 10), suggesting transient shifts in community dominance or compositional dropout. Together, these results suggest that within-group divergence across all diets was primarily driven by subtle reorganization of shared taxa (abundance changes and replacement, i.e., balanced), rather than large-scale species loss or gain.
locked for now
Cell Host and Microbe
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